Stock Market Prediction Sumaiya Aziz 201-15-3218 Abstract Prediction of stock market trends is considered as an important task and is of great attention as predicting stock prices successfully may lead to attractive profits by making proper decisions. Stock market prediction is a major challenge owing to non-stationary, blaring, and chaotic data, and thus, the prediction becomes challenging among the investors to invest the money for making profits. Several techniques are devised in the existing techniques to predict the stock market trends. This work presents the detailed review of 50 research papers suggesting the methodologies, like Bayesian model, Fuzzy classifier, (ANN), Support Vector Machine (SVM) classifier, (NN), Machine Learning Methods and so on, based on stock market prediction. The obtained papers are classified based on different prediction and clustering techniques. The research gaps and the challenges faced by the existing techniques are listed and elaborated, which help the researchers to upgrade the future works. The works are analyzed using certain datasets, performance evaluation measures, prediction techniques utilized, and performance attained by different techniques. The commonly used technique for attaining effective stock market prediction is ANN and the fuzzy-based technique. Even though a lot of research efforts, the current stock market prediction technique still have many limits. From this survey, it can be concluded that the stock market prediction is a very complex task, and different factors should be considered for predicting the future of the market more accurately and efficiently. For all investors, it is always necessary to predict stock market changes for detecting accurate profits and reducing potential mark risks. This study employed tree-based models (Decision Tree, Bagging, Random Forest, Adaboost, Gradient Boosting, and XGBoost) and neural networks (ANN, RNN, and LSTM) to correctly forecast the values of four stock market groups (Diversified Financials, Petroleum, Nonmetallic minerals, and Basic metals) as a regression problem. The predictions were made for 1, 2, 5, 10, 15, 20, and 30 days ahead. As far as our belief and knowledge, this study is the most successful and recent research work that involves ensemble learning methods and deep learning algorithms for predicting stock groups as a popular application. To be more detailed, exponentially smoothed technical indicators and features were used as inputs for prediction. In this prediction problem, the methods were able to significantly advance their performance, and LSTM was the top performer in comparison with other techniques. Overall, as a logical conclusion, both tree-based and deep learning algorithms showed remarkable potential in regression problems to predict the future values of the Tehran stock exchange. Among all models, LSTM was our superior model for predicting all stock market groups with the lowest error and the best ability to fit (by average values of MAPE: 0.60, 1.18, 1.52 and 0.54), but the problem was the great runtime (80.902 ms per sample). As future work, we recommend using the algorithms on other stock markets or examining other hyperparameters effects on the final results. References Asadi, S.; Hadavandi, E.; Mehmanpazir, F.; Nakhostin, M.M. Hybridization of evolutionary Levenberg– Marquardt neural networks and data pre-processing for stock market prediction. Knowl.-Based Syst. 2012, 35, 245–258. Akhter, S.; Misir, M.A. Capital markets efficiency: Evidence from the emerging capital market with particular reference to Dhaka stock exchange. South Asian J. Manag. 2005, 12, 35. Miao, K.; Chen, F.; Zhao, Z. Stock price forecast based on bacterial colony RBF neural network. J. Qingdao Univ. (Nat. Sci. Ed.) 2007, 2, 210–230.
